Overview

Dataset statistics

Number of variables45
Number of observations22083
Missing cells94691
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.0 MiB
Average record size in memory1.9 KiB

Variable types

CAT21
BOOL18
NUM6

Warnings

Patient_First_Name has a high cardinality: 2524 distinct values High cardinality
Family_Name has a high cardinality: 6282 distinct values High cardinality
Fathers_name has a high cardinality: 16368 distinct values High cardinality
Autopsy_shows_birth_defect_(if_applicable) is highly correlated with StatusHigh correlation
Status is highly correlated with Autopsy_shows_birth_defect_(if_applicable)High correlation
Place_of_birth is highly correlated with Institute_Name and 1 other fieldsHigh correlation
Institute_Name is highly correlated with Place_of_birthHigh correlation
Location_of_Institute is highly correlated with Place_of_birthHigh correlation
Disorder_Subclass is highly correlated with Genetic_DisorderHigh correlation
Genetic_Disorder is highly correlated with Disorder_SubclassHigh correlation
Patient_Age has 1427 (6.5%) missing values Missing
Inherited_from_father has 306 (1.4%) missing values Missing
Maternal_gene has 2810 (12.7%) missing values Missing
Family_Name has 9691 (43.9%) missing values Missing
Mothers_age has 6036 (27.3%) missing values Missing
Fathers_age has 5986 (27.1%) missing values Missing
Institute_Name has 5106 (23.1%) missing values Missing
Respiratory_Rate_(breaths/min) has 2149 (9.7%) missing values Missing
Heart_Rate_(rates/min has 2113 (9.6%) missing values Missing
Test_1 has 2127 (9.6%) missing values Missing
Test_2 has 2152 (9.7%) missing values Missing
Test_3 has 2147 (9.7%) missing values Missing
Test_4 has 2140 (9.7%) missing values Missing
Test_5 has 2170 (9.8%) missing values Missing
Parental_consent has 2125 (9.6%) missing values Missing
Follow-up has 2166 (9.8%) missing values Missing
Gender has 2173 (9.8%) missing values Missing
Birth_asphyxia has 2139 (9.7%) missing values Missing
Autopsy_shows_birth_defect_(if_applicable) has 1026 (4.6%) missing values Missing
Place_of_birth has 2124 (9.6%) missing values Missing
Folic_acid_details_(peri-conceptional) has 2117 (9.6%) missing values Missing
H/O_serious_maternal_illness has 2152 (9.7%) missing values Missing
H/O_radiation_exposure_(x-ray) has 2153 (9.7%) missing values Missing
H/O_substance_abuse has 2195 (9.9%) missing values Missing
Assisted_conception_IVF/ART has 2122 (9.6%) missing values Missing
History_of_anomalies_in_previous_pregnancies has 2172 (9.8%) missing values Missing
No._of_previous_abortion has 2162 (9.8%) missing values Missing
Birth_defects has 2154 (9.8%) missing values Missing
White_Blood_cell_count_(thousand_per_microliter) has 2148 (9.7%) missing values Missing
Blood_test_result has 2145 (9.7%) missing values Missing
Symptom_1 has 2155 (9.8%) missing values Missing
Symptom_2 has 2222 (10.1%) missing values Missing
Symptom_3 has 2101 (9.5%) missing values Missing
Symptom_4 has 2113 (9.6%) missing values Missing
Symptom_5 has 2153 (9.7%) missing values Missing
Genetic_Disorder has 2146 (9.7%) missing values Missing
Disorder_Subclass has 2168 (9.8%) missing values Missing
Fathers_name is uniformly distributed Uniform
Patient_Id has unique values Unique
Blood_cell_count_(mcL) has unique values Unique
Patient_Age has 1386 (6.3%) zeros Zeros
No._of_previous_abortion has 3964 (18.0%) zeros Zeros

Reproduction

Analysis started2021-11-04 21:44:26.453481
Analysis finished2021-11-04 21:45:20.487097
Duration54.03 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Patient_Id
Categorical

UNIQUE

Distinct22083
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size172.6 KiB
PID0x6038
 
1
PID0x68a9
 
1
PID0x2825
 
1
PID0x9921
 
1
PID0x91e
 
1
Other values (22078)
22078 
ValueCountFrequency (%) 
PID0x60381< 0.1%
 
PID0x68a91< 0.1%
 
PID0x28251< 0.1%
 
PID0x99211< 0.1%
 
PID0x91e1< 0.1%
 
PID0x3d091< 0.1%
 
PID0x859e1< 0.1%
 
PID0x18681< 0.1%
 
PID0x22831< 0.1%
 
PID0x8011< 0.1%
 
PID0x5e4e1< 0.1%
 
PID0x7b991< 0.1%
 
PID0x70aa1< 0.1%
 
PID0x125b1< 0.1%
 
PID0x4e4f1< 0.1%
 
PID0x281c1< 0.1%
 
PID0x434c1< 0.1%
 
PID0x69491< 0.1%
 
PID0x2dcf1< 0.1%
 
PID0xe1b1< 0.1%
 
PID0x6b5f1< 0.1%
 
PID0x250e1< 0.1%
 
PID0x45441< 0.1%
 
PID0x64ed1< 0.1%
 
PID0x37681< 0.1%
 
Other values (22058)2205899.9%
 
2021-11-04T17:45:20.876561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique22083 ?
Unique (%)100.0%
2021-11-04T17:45:21.122711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.894579541
Min length6

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
02615813.3%
 
P2208311.2%
 
I2208311.2%
 
D2208311.2%
 
x2208311.2%
 
665043.3%
 
764863.3%
 
864693.3%
 
564433.3%
 
464393.3%
 
364273.3%
 
164153.3%
 
264053.3%
 
959173.0%
 
a42212.1%
 
b41062.1%
 
f40582.1%
 
d40332.1%
 
e40092.0%
 
c39972.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number8366342.6%
 
Uppercase Letter6624933.7%
 
Lowercase Letter4650723.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P2208333.3%
 
I2208333.3%
 
D2208333.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
02615831.3%
 
665047.8%
 
764867.8%
 
864697.7%
 
564437.7%
 
464397.7%
 
364277.7%
 
164157.7%
 
264057.7%
 
959177.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
x2208347.5%
 
a42219.1%
 
b41068.8%
 
f40588.7%
 
d40338.7%
 
e40098.6%
 
c39978.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin11275657.4%
 
Common8366342.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
P2208319.6%
 
I2208319.6%
 
D2208319.6%
 
x2208319.6%
 
a42213.7%
 
b41063.6%
 
f40583.6%
 
d40333.6%
 
e40093.6%
 
c39973.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
02615831.3%
 
665047.8%
 
764867.8%
 
864697.7%
 
564437.7%
 
464397.7%
 
364277.7%
 
164157.7%
 
264057.7%
 
959177.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII196419100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
02615813.3%
 
P2208311.2%
 
I2208311.2%
 
D2208311.2%
 
x2208311.2%
 
665043.3%
 
764863.3%
 
864693.3%
 
564433.3%
 
464393.3%
 
364273.3%
 
164153.3%
 
264053.3%
 
959173.0%
 
a42212.1%
 
b41062.1%
 
f40582.1%
 
d40332.1%
 
e40092.0%
 
c39972.0%
 

Patient_Age
Real number (ℝ≥0)

MISSING
ZEROS

Distinct15
Distinct (%)0.1%
Missing1427
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean6.974147947
Minimum0
Maximum14
Zeros1386
Zeros (%)6.3%
Memory size172.6 KiB
2021-11-04T17:45:21.328852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.319475047
Coefficient of variation (CV)0.6193552359
Kurtosis-1.215597674
Mean6.974147947
Median Absolute Deviation (MAD)4
Skewness0.00950741439
Sum144058
Variance18.65786468
MonotocityNot monotonic
2021-11-04T17:45:21.527734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
414356.5%
 
1214356.5%
 
914156.4%
 
213966.3%
 
513946.3%
 
013866.3%
 
1313846.3%
 
313836.3%
 
613746.2%
 
113646.2%
 
1113536.1%
 
713516.1%
 
813406.1%
 
1413336.0%
 
1013135.9%
 
(Missing)14276.5%
 
ValueCountFrequency (%) 
013866.3%
 
113646.2%
 
213966.3%
 
313836.3%
 
414356.5%
 
513946.3%
 
613746.2%
 
713516.1%
 
813406.1%
 
914156.4%
 
ValueCountFrequency (%) 
1413336.0%
 
1313846.3%
 
1214356.5%
 
1113536.1%
 
1013135.9%
 
914156.4%
 
813406.1%
 
713516.1%
 
613746.2%
 
513946.3%
 

Genes_in_mothers_side
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size172.6 KiB
Yes
13143 
No
8940 
ValueCountFrequency (%) 
Yes1314359.5%
 
No894040.5%
 
2021-11-04T17:45:21.682165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Inherited_from_father
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing306
Missing (%)1.4%
Memory size172.6 KiB
No
13133 
Yes
8644 
(Missing)
 
306
ValueCountFrequency (%) 
No1313359.5%
 
Yes864439.1%
 
(Missing)3061.4%
 
2021-11-04T17:45:21.752619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Maternal_gene
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2810
Missing (%)12.7%
Memory size172.6 KiB
Yes
10647 
No
8626 
(Missing)
2810 
ValueCountFrequency (%) 
Yes1064748.2%
 
No862639.1%
 
(Missing)281012.7%
 
2021-11-04T17:45:21.824763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Paternal_gene
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size172.6 KiB
No
12508 
Yes
9575 
ValueCountFrequency (%) 
No1250856.6%
 
Yes957543.4%
 
2021-11-04T17:45:21.892454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Blood_cell_count_(mcL)
Real number (ℝ≥0)

UNIQUE

Distinct22083
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.898871078
Minimum4.092727034
Maximum5.60982897
Zeros0
Zeros (%)0.0%
Memory size172.6 KiB
2021-11-04T17:45:22.060481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.092727034
5-th percentile4.570279662
Q14.763108642
median4.899398761
Q35.033830033
95-th percentile5.228652022
Maximum5.60982897
Range1.517101936
Interquartile range (IQR)0.2707213911

Descriptive statistics

Standard deviation0.1996628593
Coefficient of variation (CV)0.04075691238
Kurtosis-0.06282141886
Mean4.898871078
Median Absolute Deviation (MAD)0.1352425499
Skewness0.01002341388
Sum108181.77
Variance0.03986525739
MonotocityNot monotonic
2021-11-04T17:45:22.340075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.1215113381< 0.1%
 
4.5423962151< 0.1%
 
4.9774244211< 0.1%
 
4.3149076561< 0.1%
 
4.633808151< 0.1%
 
4.8733294581< 0.1%
 
4.7530015041< 0.1%
 
4.7027505331< 0.1%
 
4.7930142891< 0.1%
 
4.6601018081< 0.1%
 
4.8754460151< 0.1%
 
5.0118911041< 0.1%
 
4.6648395341< 0.1%
 
4.998807261< 0.1%
 
4.9639894121< 0.1%
 
4.9996736771< 0.1%
 
5.0635420571< 0.1%
 
4.9345904311< 0.1%
 
4.8217670171< 0.1%
 
4.9941437251< 0.1%
 
4.9024999331< 0.1%
 
4.7908356261< 0.1%
 
4.7384222131< 0.1%
 
5.1963559411< 0.1%
 
4.6437019091< 0.1%
 
Other values (22058)2205899.9%
 
ValueCountFrequency (%) 
4.0927270341< 0.1%
 
4.1462298151< 0.1%
 
4.1858211051< 0.1%
 
4.2034641641< 0.1%
 
4.2155990361< 0.1%
 
4.235726631< 0.1%
 
4.2485653521< 0.1%
 
4.2502124961< 0.1%
 
4.2587987241< 0.1%
 
4.2647957141< 0.1%
 
ValueCountFrequency (%) 
5.609828971< 0.1%
 
5.5924507071< 0.1%
 
5.5740966721< 0.1%
 
5.5719664751< 0.1%
 
5.5699020741< 0.1%
 
5.5642121581< 0.1%
 
5.5589325751< 0.1%
 
5.5539515641< 0.1%
 
5.5364037021< 0.1%
 
5.5327822971< 0.1%
 

Patient_First_Name
Categorical

HIGH CARDINALITY

Distinct2524
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size172.6 KiB
James
 
420
John
 
372
Robert
 
355
Mary
 
329
Michael
 
321
Other values (2519)
20286 
ValueCountFrequency (%) 
James4201.9%
 
John3721.7%
 
Robert3551.6%
 
Mary3291.5%
 
Michael3211.5%
 
David2881.3%
 
William2871.3%
 
Charles1960.9%
 
Richard1840.8%
 
Thomas1740.8%
 
Joseph1710.8%
 
Barbara1300.6%
 
Donald1220.6%
 
Jennifer1200.5%
 
Patricia1190.5%
 
Daniel1150.5%
 
Elizabeth1110.5%
 
Linda1080.5%
 
Mark1080.5%
 
Maria1070.5%
 
George1060.5%
 
Paul1060.5%
 
Margaret1050.5%
 
Christopher1040.5%
 
Dorothy1000.5%
 
Other values (2499)1742578.9%
 
2021-11-04T17:45:23.078431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1021 ?
Unique (%)4.6%
2021-11-04T17:45:23.839145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length6
Mean length5.794049722
Min length2

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1524811.9%
 
e1404811.0%
 
r100147.8%
 
n98197.7%
 
i92287.2%
 
l74035.8%
 
o62624.9%
 
t48503.8%
 
h47233.7%
 
s40973.2%
 
y40023.1%
 
d32812.6%
 
J29622.3%
 
c27022.1%
 
m23161.8%
 
M21971.7%
 
u18571.5%
 
R17511.4%
 
D16451.3%
 
C15741.2%
 
A14061.1%
 
S13791.1%
 
b13541.1%
 
L12351.0%
 
B10610.8%
 
Other values (27)115369.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10586782.7%
 
Uppercase Letter2208317.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J296213.4%
 
M21979.9%
 
R17517.9%
 
D16457.4%
 
C15747.1%
 
A14066.4%
 
S13796.2%
 
L12355.6%
 
B10614.8%
 
E9954.5%
 
K9324.2%
 
T9284.2%
 
G6883.1%
 
P6843.1%
 
W6232.8%
 
H5342.4%
 
N3971.8%
 
F3801.7%
 
V3401.5%
 
I1640.7%
 
O1080.5%
 
Y570.3%
 
Z220.1%
 
Q10< 0.1%
 
U9< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1524814.4%
 
e1404813.3%
 
r100149.5%
 
n98199.3%
 
i92288.7%
 
l74037.0%
 
o62625.9%
 
t48504.6%
 
h47234.5%
 
s40973.9%
 
y40023.8%
 
d32813.1%
 
c27022.6%
 
m23162.2%
 
u18571.8%
 
b13541.3%
 
v9490.9%
 
g9320.9%
 
f6270.6%
 
p6060.6%
 
k5800.5%
 
w5240.5%
 
z2150.2%
 
x1030.1%
 
j730.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin127950100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1524811.9%
 
e1404811.0%
 
r100147.8%
 
n98197.7%
 
i92287.2%
 
l74035.8%
 
o62624.9%
 
t48503.8%
 
h47233.7%
 
s40973.2%
 
y40023.1%
 
d32812.6%
 
J29622.3%
 
c27022.1%
 
m23161.8%
 
M21971.7%
 
u18571.5%
 
R17511.4%
 
D16451.3%
 
C15741.2%
 
A14061.1%
 
S13791.1%
 
b13541.1%
 
L12351.0%
 
B10610.8%
 
Other values (27)115369.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII127950100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1524811.9%
 
e1404811.0%
 
r100147.8%
 
n98197.7%
 
i92287.2%
 
l74035.8%
 
o62624.9%
 
t48503.8%
 
h47233.7%
 
s40973.2%
 
y40023.1%
 
d32812.6%
 
J29622.3%
 
c27022.1%
 
m23161.8%
 
M21971.7%
 
u18571.5%
 
R17511.4%
 
D16451.3%
 
C15741.2%
 
A14061.1%
 
S13791.1%
 
b13541.1%
 
L12351.0%
 
B10610.8%
 
Other values (27)115369.0%
 

Family_Name
Categorical

HIGH CARDINALITY
MISSING

Distinct6282
Distinct (%)50.7%
Missing9691
Missing (%)43.9%
Memory size172.6 KiB
Smith
 
157
Williams
 
106
Johnson
 
99
Brown
 
90
Jones
 
81
Other values (6277)
11859 
ValueCountFrequency (%) 
Smith1570.7%
 
Williams1060.5%
 
Johnson990.4%
 
Brown900.4%
 
Jones810.4%
 
Davis610.3%
 
Miller540.2%
 
Wilson500.2%
 
White490.2%
 
Harris480.2%
 
Jackson470.2%
 
Anderson380.2%
 
Taylor380.2%
 
Martin360.2%
 
Moore360.2%
 
Garcia350.2%
 
Thomas340.2%
 
Walker340.2%
 
Lee330.1%
 
Clark310.1%
 
Young310.1%
 
Hall300.1%
 
Martinez290.1%
 
Mitchell280.1%
 
Roberts270.1%
 
Other values (6257)1109050.2%
 
(Missing)969143.9%
 
2021-11-04T17:45:24.252427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4556 ?
Unique (%)36.8%
2021-11-04T17:45:24.696725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length4
Mean length4.840239098
Min length2

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2537223.7%
 
a1615315.1%
 
e80057.5%
 
r62405.8%
 
o55195.2%
 
l47204.4%
 
i44864.2%
 
s40983.8%
 
t31593.0%
 
h20111.9%
 
d18361.7%
 
u17371.6%
 
c17281.6%
 
m16781.6%
 
y13701.3%
 
M12461.2%
 
g12261.1%
 
S11361.1%
 
B11241.1%
 
k10881.0%
 
H9560.9%
 
C9480.9%
 
b7990.7%
 
w7700.7%
 
W7670.7%
 
Other values (27)87158.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter9449588.4%
 
Uppercase Letter1239211.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2537226.9%
 
a1615317.1%
 
e80058.5%
 
r62406.6%
 
o55195.8%
 
l47205.0%
 
i44864.7%
 
s40984.3%
 
t31593.3%
 
h20112.1%
 
d18361.9%
 
u17371.8%
 
c17281.8%
 
m16781.8%
 
y13701.4%
 
g12261.3%
 
k10881.2%
 
b7990.8%
 
w7700.8%
 
z6950.7%
 
p6570.7%
 
v5210.6%
 
f4000.4%
 
x1150.1%
 
j560.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M124610.1%
 
S11369.2%
 
B11249.1%
 
H9567.7%
 
C9487.7%
 
W7676.2%
 
R7396.0%
 
G6465.2%
 
P6345.1%
 
L5594.5%
 
D5584.5%
 
A4383.5%
 
F4253.4%
 
T4183.4%
 
J4123.3%
 
K3703.0%
 
N2422.0%
 
E2181.8%
 
O1841.5%
 
V1561.3%
 
Y630.5%
 
Z580.5%
 
I480.4%
 
U290.2%
 
Q160.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin106887100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2537223.7%
 
a1615315.1%
 
e80057.5%
 
r62405.8%
 
o55195.2%
 
l47204.4%
 
i44864.2%
 
s40983.8%
 
t31593.0%
 
h20111.9%
 
d18361.7%
 
u17371.6%
 
c17281.6%
 
m16781.6%
 
y13701.3%
 
M12461.2%
 
g12261.1%
 
S11361.1%
 
B11241.1%
 
k10881.0%
 
H9560.9%
 
C9480.9%
 
b7990.7%
 
w7700.7%
 
W7670.7%
 
Other values (27)87158.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII106887100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2537223.7%
 
a1615315.1%
 
e80057.5%
 
r62405.8%
 
o55195.2%
 
l47204.4%
 
i44864.2%
 
s40983.8%
 
t31593.0%
 
h20111.9%
 
d18361.7%
 
u17371.6%
 
c17281.6%
 
m16781.6%
 
y13701.3%
 
M12461.2%
 
g12261.1%
 
S11361.1%
 
B11241.1%
 
k10881.0%
 
H9560.9%
 
C9480.9%
 
b7990.7%
 
w7700.7%
 
W7670.7%
 
Other values (27)87158.2%
 

Fathers_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct16368
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size172.6 KiB
Clardie
 
6
Sager
 
6
Muhib
 
5
Hafiz
 
5
Nashon
 
5
Other values (16363)
22056 
ValueCountFrequency (%) 
Clardie6< 0.1%
 
Sager6< 0.1%
 
Muhib5< 0.1%
 
Hafiz5< 0.1%
 
Nashon5< 0.1%
 
Wilder5< 0.1%
 
Lucciano5< 0.1%
 
Marell5< 0.1%
 
Edwen5< 0.1%
 
Daiquon5< 0.1%
 
True5< 0.1%
 
Buzzy5< 0.1%
 
Daric4< 0.1%
 
Williaa4< 0.1%
 
Royston4< 0.1%
 
Maurisio4< 0.1%
 
Ajai4< 0.1%
 
Dhahran4< 0.1%
 
Zantavious4< 0.1%
 
Linwood4< 0.1%
 
Kieffer4< 0.1%
 
Trezden4< 0.1%
 
Olufemi4< 0.1%
 
Reiter4< 0.1%
 
Jamesanthony4< 0.1%
 
Other values (16343)2196999.5%
 
2021-11-04T17:45:25.070077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11741 ?
Unique (%)53.2%
2021-11-04T17:45:25.610218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length6
Mean length6.289181723
Min length2

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1648011.9%
 
e135999.8%
 
n116728.4%
 
i99517.2%
 
r99397.2%
 
o82666.0%
 
l66254.8%
 
s50163.6%
 
h45343.3%
 
y39602.9%
 
d38462.8%
 
t38422.8%
 
u36732.6%
 
m33712.4%
 
J23091.7%
 
c20651.5%
 
A19461.4%
 
D18941.4%
 
v18681.3%
 
k18411.3%
 
K16001.2%
 
T14731.1%
 
M13681.0%
 
S13431.0%
 
C12470.9%
 
Other values (27)1515610.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter11680184.1%
 
Uppercase Letter2208315.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J230910.5%
 
A19468.8%
 
D18948.6%
 
K16007.2%
 
T14736.7%
 
M13686.2%
 
S13436.1%
 
C12475.6%
 
R12035.4%
 
B9484.3%
 
L9204.2%
 
E8343.8%
 
N6512.9%
 
H6062.7%
 
G5942.7%
 
Z4962.2%
 
O3911.8%
 
W3611.6%
 
P3291.5%
 
Y3271.5%
 
F3131.4%
 
V2961.3%
 
I2881.3%
 
Q1940.9%
 
U850.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1648014.1%
 
e1359911.6%
 
n1167210.0%
 
i99518.5%
 
r99398.5%
 
o82667.1%
 
l66255.7%
 
s50164.3%
 
h45343.9%
 
y39603.4%
 
d38463.3%
 
t38423.3%
 
u36733.1%
 
m33712.9%
 
c20651.8%
 
v18681.6%
 
k18411.6%
 
b11841.0%
 
g9030.8%
 
z8980.8%
 
w7680.7%
 
j6500.6%
 
f6230.5%
 
p5090.4%
 
q4200.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin138884100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1648011.9%
 
e135999.8%
 
n116728.4%
 
i99517.2%
 
r99397.2%
 
o82666.0%
 
l66254.8%
 
s50163.6%
 
h45343.3%
 
y39602.9%
 
d38462.8%
 
t38422.8%
 
u36732.6%
 
m33712.4%
 
J23091.7%
 
c20651.5%
 
A19461.4%
 
D18941.4%
 
v18681.3%
 
k18411.3%
 
K16001.2%
 
T14731.1%
 
M13681.0%
 
S13431.0%
 
C12470.9%
 
Other values (27)1515610.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII138884100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1648011.9%
 
e135999.8%
 
n116728.4%
 
i99517.2%
 
r99397.2%
 
o82666.0%
 
l66254.8%
 
s50163.6%
 
h45343.3%
 
y39602.9%
 
d38462.8%
 
t38422.8%
 
u36732.6%
 
m33712.4%
 
J23091.7%
 
c20651.5%
 
A19461.4%
 
D18941.4%
 
v18681.3%
 
k18411.3%
 
K16001.2%
 
T14731.1%
 
M13681.0%
 
S13431.0%
 
C12470.9%
 
Other values (27)1515610.9%
 

Mothers_age
Real number (ℝ≥0)

MISSING

Distinct34
Distinct (%)0.2%
Missing6036
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean34.52645354
Minimum18
Maximum51
Zeros0
Zeros (%)0.0%
Memory size172.6 KiB
2021-11-04T17:45:25.916213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q126
median35
Q343
95-th percentile50
Maximum51
Range33
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.852598421
Coefficient of variation (CV)0.2853637548
Kurtosis-1.223088083
Mean34.52645354
Median Absolute Deviation (MAD)9
Skewness-0.005153871873
Sum554046
Variance97.07369565
MonotocityNot monotonic
2021-11-04T17:45:26.352662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
235252.4%
 
195162.3%
 
405152.3%
 
285082.3%
 
475082.3%
 
485072.3%
 
415022.3%
 
454902.2%
 
444892.2%
 
214882.2%
 
354842.2%
 
244802.2%
 
494792.2%
 
504762.2%
 
304732.1%
 
274712.1%
 
294692.1%
 
324652.1%
 
384652.1%
 
424642.1%
 
374632.1%
 
224632.1%
 
464602.1%
 
264572.1%
 
314572.1%
 
Other values (9)397318.0%
 
(Missing)603627.3%
 
ValueCountFrequency (%) 
184432.0%
 
195162.3%
 
204512.0%
 
214882.2%
 
224632.1%
 
235252.4%
 
244802.2%
 
254352.0%
 
264572.1%
 
274712.1%
 
ValueCountFrequency (%) 
514492.0%
 
504762.2%
 
494792.2%
 
485072.3%
 
475082.3%
 
464602.1%
 
454902.2%
 
444892.2%
 
434372.0%
 
424642.1%
 

Fathers_age
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)0.3%
Missing5986
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean41.97285208
Minimum20
Maximum64
Zeros0
Zeros (%)0.0%
Memory size172.6 KiB
2021-11-04T17:45:26.679830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q131
median42
Q353
95-th percentile62
Maximum64
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.03550058
Coefficient of variation (CV)0.3105698072
Kurtosis-1.213855517
Mean41.97285208
Median Absolute Deviation (MAD)11
Skewness-0.005839712015
Sum675637
Variance169.9242754
MonotocityNot monotonic
2021-11-04T17:45:26.978423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
204141.9%
 
494001.8%
 
293991.8%
 
613961.8%
 
573811.7%
 
393801.7%
 
563781.7%
 
533771.7%
 
273771.7%
 
303771.7%
 
443721.7%
 
263711.7%
 
383701.7%
 
523691.7%
 
643681.7%
 
323651.7%
 
403631.6%
 
373631.6%
 
513631.6%
 
593611.6%
 
233601.6%
 
283581.6%
 
583581.6%
 
503571.6%
 
313561.6%
 
Other values (20)676430.6%
 
(Missing)598627.1%
 
ValueCountFrequency (%) 
204141.9%
 
213501.6%
 
223391.5%
 
233601.6%
 
243491.6%
 
253231.5%
 
263711.7%
 
273771.7%
 
283581.6%
 
293991.8%
 
ValueCountFrequency (%) 
643681.7%
 
633141.4%
 
623471.6%
 
613961.8%
 
603451.6%
 
593611.6%
 
583581.6%
 
573811.7%
 
563781.7%
 
553471.6%
 

Institute_Name
Categorical

HIGH CORRELATION
MISSING

Distinct27
Distinct (%)0.2%
Missing5106
Missing (%)23.1%
Memory size172.6 KiB
Not applicable
8440 
Franciscan Children's Hospital
 
363
Carney Hospital
 
357
New England Medical Center
 
350
Hebrew Rehabilitation Center
 
349
Other values (22)
7118 
ValueCountFrequency (%) 
Not applicable844038.2%
 
Franciscan Children's Hospital3631.6%
 
Carney Hospital3571.6%
 
New England Medical Center3501.6%
 
Hebrew Rehabilitation Center3491.6%
 
VA Hospital3441.6%
 
Shriners Burns Institute3411.5%
 
Massachusetts Eye & Ear Infirmary3371.5%
 
Brigham And Women's Hospital3341.5%
 
Boston City Hospital3301.5%
 
St. Margaret's Hospital For Women3291.5%
 
Arbour Hospital3271.5%
 
Spaulding Rehabilitation Hospital3251.5%
 
Faulkner Hospital3251.5%
 
Children's Hospital3241.5%
 
Kindred Hospital3241.5%
 
Dana-farber Cancer Institute3231.5%
 
Boston Specialty & Rehabilitation Hospital3221.5%
 
Massachusetts General Hospital3211.5%
 
Beth Israel Deaconess Medical Center East Cam3201.4%
 
Boston Medical Center3181.4%
 
New England Baptist Hospital3171.4%
 
Jewish Memorial Hospital3151.4%
 
Beth Israel Deaconess Medical Center West Cam3151.4%
 
Lemuel Shattuck Hospital3131.4%
 
Other values (2)6142.8%
 
(Missing)510623.1%
 
2021-11-04T17:45:27.274923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:27.515485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length45
Median length14
Mean length16.00461894
Min length3

Overview of Unicode Properties

Unique unicode characters45
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a4226612.0%
 
l292758.3%
 
290818.2%
 
t266907.6%
 
e261167.4%
 
i238916.8%
 
p237286.7%
 
n221906.3%
 
o195295.5%
 
s160194.5%
 
c127203.6%
 
b107373.0%
 
r103062.9%
 
N91072.6%
 
H62331.8%
 
h45811.3%
 
C39841.1%
 
d39641.1%
 
u32660.9%
 
M26050.7%
 
B25970.7%
 
m25970.7%
 
E19630.6%
 
S19320.5%
 
y16830.5%
 
Other values (20)163704.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter28414480.4%
 
Uppercase Letter3694010.5%
 
Space Separator290818.2%
 
Other Punctuation29420.8%
 
Dash Punctuation3230.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N910724.7%
 
H623316.9%
 
C398410.8%
 
M26057.1%
 
B25977.0%
 
E19635.3%
 
S19325.2%
 
I16364.4%
 
F10172.8%
 
A10052.7%
 
R9962.7%
 
W9782.6%
 
D9582.6%
 
V6561.8%
 
K3240.9%
 
G3210.9%
 
J3150.9%
 
L3130.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a4226614.9%
 
l2927510.3%
 
t266909.4%
 
e261169.2%
 
i238918.4%
 
p237288.4%
 
n221907.8%
 
o195296.9%
 
s160195.6%
 
c127204.5%
 
b107373.8%
 
r103063.6%
 
h45811.6%
 
d39641.4%
 
u32661.1%
 
m25970.9%
 
y16830.6%
 
g16550.6%
 
w13310.5%
 
f6600.2%
 
k6380.2%
 
z3020.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
29081100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'165256.2%
 
&65922.4%
 
.63121.4%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-323100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin32108490.8%
 
Common323469.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a4226613.2%
 
l292759.1%
 
t266908.3%
 
e261168.1%
 
i238917.4%
 
p237287.4%
 
n221906.9%
 
o195296.1%
 
s160195.0%
 
c127204.0%
 
b107373.3%
 
r103063.2%
 
N91072.8%
 
H62331.9%
 
h45811.4%
 
C39841.2%
 
d39641.2%
 
u32661.0%
 
M26050.8%
 
B25970.8%
 
m25970.8%
 
E19630.6%
 
S19320.6%
 
y16830.5%
 
g16550.5%
 
Other values (15)114503.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
2908189.9%
 
'16525.1%
 
&6592.0%
 
.6312.0%
 
-3231.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII353430100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a4226612.0%
 
l292758.3%
 
290818.2%
 
t266907.6%
 
e261167.4%
 
i238916.8%
 
p237286.7%
 
n221906.3%
 
o195295.5%
 
s160194.5%
 
c127203.6%
 
b107373.0%
 
r103062.9%
 
N91072.6%
 
H62331.8%
 
h45811.3%
 
C39841.1%
 
d39641.1%
 
u32660.9%
 
M26050.7%
 
B25970.7%
 
m25970.7%
 
E19630.6%
 
S19320.5%
 
y16830.5%
 
Other values (20)163704.6%
 

Location_of_Institute
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size172.6 KiB
-
10931 
125 PARKER HILL AV JAMAICA PLAIN, MA 02120 (42.329611374844326, -71.10616871232227)
 
864
249 RIVER ST MATTAPAN, MA 02126 (42.27137912172521, -71.08168028446168)
 
466
2100 DORCHESTER AV DORCHESTER, MA 02124 (42.27854306401838, -71.06631280050811)
 
458
1200 Centre St Roslindale, MA 02131 (42.29738386053219, -71.13150465441208)
 
455
Other values (21)
8909 
ValueCountFrequency (%) 
-1093149.5%
 
125 PARKER HILL AV JAMAICA PLAIN, MA 02120 (42.329611374844326, -71.10616871232227)8643.9%
 
249 RIVER ST MATTAPAN, MA 02126 (42.27137912172521, -71.08168028446168)4662.1%
 
2100 DORCHESTER AV DORCHESTER, MA 02124 (42.27854306401838, -71.06631280050811)4582.1%
 
1200 Centre St Roslindale, MA 02131 (42.29738386053219, -71.13150465441208)4552.1%
 
51 BLOSSOM ST CENTRAL, MA 02114 (42.36327718561898, -71.0668523937257)4512.0%
 
736 CAMBRIDGE ST ALLSTON/BRIGHTON, MA 02135 (42.349656455743144, -71.14822103232248)4462.0%
 
75 FRANCIS ST FENWAY/KENMORE, MA 02115 (42.33587602903896, -71.10741054246668)4432.0%
 
818 HARRISON AV SOUTH END, MA 02118 (42.335925371008436, -71.07378404269969)4432.0%
 
59 TOWNSEND ST ROXBURY, MA 02119 (42.31856289432221, -71.09165569529381)4422.0%
 
1400 VFW Parkway West Roxbury, MA 02132 (42.27598935537618, -71.17245195460838)4412.0%
 
90 CUSHING AV DORCHESTER, MA 02125 (42.314030311294516, -71.06406449543488)4402.0%
 
300 LONGWOOD AV FENWAY/KENMORE, MA 02115 (42.337592548462226, -71.10472284437952)4362.0%
 
30 WARREN ST ALLSTON/BRIGHTON, MA 02134 (42.352620000312925, -71.13281000028115)4342.0%
 
185 PILGRIM RD FENWAY/KENMORE, MA 02215 (42.3385289546495, -71.10940050507557)4332.0%
 
44 BINNEY ST FENWAY/KENMORE, MA 02115 (42.33734993862189, -71.1071702648531)4231.9%
 
125 NASHUA ST CENTRAL, MA 02114 (42.36764789068138, -71.06564730220646)4211.9%
 
750 WASHINGTON ST CENTRAL, MA 02111 (42.349946522039204, -71.0634111017112)4201.9%
 
330 BROOKLINE AV FENWAY/KENMORE, MA 02115 (42.3438499996779, -71.08983000035408)4171.9%
 
1515 COMMONWEALTH AV ALLSTON/BRIGHTON, MA 02135 (42.34665771451756, -71.14136122385321)4171.9%
 
55 FRUIT ST CENTRAL, MA 02114 (42.36247485742686, -71.06924724545246)4121.9%
 
243 CHARLES ST CENTRAL, MA 02114 (42.36297141612903, -71.07043169540236)4121.9%
 
88 EAST NEWTON ST SOUTH END, MA 02118 (42.3371094801158, -71.07139912234962)4031.8%
 
49 ROBINWOOD AV JAMAICA PLAIN, MA 02130 (42.31617666213941, -71.11272670363542)4021.8%
 
170 MORTON ST ROSLINDALE, MA 02130 (42.30025000839615, -71.10737910445549)3881.8%
 
2021-11-04T17:45:27.770723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:28.027243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length87
Median length69
Mean length39.35891863
Min length1

Overview of Unicode Properties

Unique unicode characters58
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1716318.2%
 
2666837.7%
 
599656.9%
 
0525756.0%
 
4489605.6%
 
3440215.1%
 
7359844.1%
 
5348004.0%
 
6343414.0%
 
A341083.9%
 
8302653.5%
 
9260623.0%
 
223042.6%
 
,223042.6%
 
.223042.6%
 
-220832.5%
 
N203672.3%
 
R190302.2%
 
E184152.1%
 
M179732.1%
 
T170602.0%
 
O153461.8%
 
S146141.7%
 
L114311.3%
 
(111521.3%
 
Other values (33)9538511.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number44532251.2%
 
Uppercase Letter23568827.1%
 
Space Separator599656.9%
 
Other Punctuation480575.5%
 
Control223042.6%
 
Dash Punctuation220832.5%
 
Lowercase Letter134401.5%
 
Open Punctuation111521.3%
 
Close Punctuation111521.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
17163116.1%
 
26668315.0%
 
05257511.8%
 
44896011.0%
 
3440219.9%
 
7359848.1%
 
5348007.8%
 
6343417.7%
 
8302656.8%
 
9260625.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
59965100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A3410814.5%
 
N203678.6%
 
R190308.1%
 
E184157.8%
 
M179737.6%
 
T170607.2%
 
O153466.5%
 
S146146.2%
 
L114314.9%
 
I110294.7%
 
C81213.4%
 
H69162.9%
 
W59882.5%
 
V47842.0%
 
D47492.0%
 
B38781.6%
 
P38551.6%
 
G34721.5%
 
F34481.5%
 
K34331.5%
 
Y30171.3%
 
U25611.1%
 
J16510.7%
 
X4420.2%
 

Most frequent Control characters

ValueCountFrequency (%) 
22304100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,2230446.4%
 
.2230446.4%
 
/34497.2%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(11152100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-22083100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)11152100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e180613.4%
 
t135110.1%
 
r13379.9%
 
a13379.9%
 
n9106.8%
 
l9106.8%
 
o8966.7%
 
s8966.7%
 
y8826.6%
 
i4553.4%
 
d4553.4%
 
k4413.3%
 
w4413.3%
 
x4413.3%
 
b4413.3%
 
u4413.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common62003571.3%
 
Latin24912828.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
17163111.6%
 
26668310.8%
 
599659.7%
 
0525758.5%
 
4489607.9%
 
3440217.1%
 
7359845.8%
 
5348005.6%
 
6343415.5%
 
8302654.9%
 
9260624.2%
 
223043.6%
 
,223043.6%
 
.223043.6%
 
-220833.6%
 
(111521.8%
 
)111521.8%
 
/34490.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A3410813.7%
 
N203678.2%
 
R190307.6%
 
E184157.4%
 
M179737.2%
 
T170606.8%
 
O153466.2%
 
S146145.9%
 
L114314.6%
 
I110294.4%
 
C81213.3%
 
H69162.8%
 
W59882.4%
 
V47841.9%
 
D47491.9%
 
B38781.6%
 
P38551.5%
 
G34721.4%
 
F34481.4%
 
K34331.4%
 
Y30171.2%
 
U25611.0%
 
e18060.7%
 
J16510.7%
 
t13510.5%
 
Other values (15)107254.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII869163100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1716318.2%
 
2666837.7%
 
599656.9%
 
0525756.0%
 
4489605.6%
 
3440215.1%
 
7359844.1%
 
5348004.0%
 
6343414.0%
 
A341083.9%
 
8302653.5%
 
9260623.0%
 
223042.6%
 
,223042.6%
 
.223042.6%
 
-220832.5%
 
N203672.3%
 
R190302.2%
 
E184152.1%
 
M179732.1%
 
T170602.0%
 
O153461.8%
 
S146141.7%
 
L114311.3%
 
(111521.3%
 
Other values (33)9538511.0%
 

Status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size172.6 KiB
Alive
11083 
Deceased
11000 
ValueCountFrequency (%) 
Alive1108350.2%
 
Deceased1100049.8%
 
2021-11-04T17:45:28.250327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:28.400023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:28.557729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length5
Mean length6.494362179
Min length5

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e4408330.7%
 
A110837.7%
 
l110837.7%
 
i110837.7%
 
v110837.7%
 
D110007.7%
 
c110007.7%
 
a110007.7%
 
s110007.7%
 
d110007.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12133284.6%
 
Uppercase Letter2208315.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1108350.2%
 
D1100049.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e4408336.3%
 
l110839.1%
 
i110839.1%
 
v110839.1%
 
c110009.1%
 
a110009.1%
 
s110009.1%
 
d110009.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin143415100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e4408330.7%
 
A110837.7%
 
l110837.7%
 
i110837.7%
 
v110837.7%
 
D110007.7%
 
c110007.7%
 
a110007.7%
 
s110007.7%
 
d110007.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII143415100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e4408330.7%
 
A110837.7%
 
l110837.7%
 
i110837.7%
 
v110837.7%
 
D110007.7%
 
c110007.7%
 
a110007.7%
 
s110007.7%
 
d110007.7%
 

Respiratory_Rate_(breaths/min)
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2149
Missing (%)9.7%
Memory size172.6 KiB
Normal (30-60)
10065 
Tachypnea
9869 
ValueCountFrequency (%) 
Normal (30-60)1006545.6%
 
Tachypnea986944.7%
 
(Missing)21499.7%
 
2021-11-04T17:45:28.919387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:29.075026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:29.356401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length9
Mean length10.69501426
Min length3

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a3195213.5%
 
0201308.5%
 
n141676.0%
 
N100654.3%
 
o100654.3%
 
r100654.3%
 
m100654.3%
 
l100654.3%
 
100654.3%
 
(100654.3%
 
3100654.3%
 
-100654.3%
 
6100654.3%
 
)100654.3%
 
T98694.2%
 
c98694.2%
 
h98694.2%
 
y98694.2%
 
p98694.2%
 
e98694.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter13572457.5%
 
Decimal Number4026017.0%
 
Uppercase Letter199348.4%
 
Space Separator100654.3%
 
Open Punctuation100654.3%
 
Dash Punctuation100654.3%
 
Close Punctuation100654.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1006550.5%
 
T986949.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a3195223.5%
 
n1416710.4%
 
o100657.4%
 
r100657.4%
 
m100657.4%
 
l100657.4%
 
c98697.3%
 
h98697.3%
 
y98697.3%
 
p98697.3%
 
e98697.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10065100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(10065100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
02013050.0%
 
31006525.0%
 
61006525.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-10065100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)10065100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin15565865.9%
 
Common8052034.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a3195220.5%
 
n141679.1%
 
N100656.5%
 
o100656.5%
 
r100656.5%
 
m100656.5%
 
l100656.5%
 
T98696.3%
 
c98696.3%
 
h98696.3%
 
y98696.3%
 
p98696.3%
 
e98696.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
02013025.0%
 
1006512.5%
 
(1006512.5%
 
31006512.5%
 
-1006512.5%
 
61006512.5%
 
)1006512.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII236178100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a3195213.5%
 
0201308.5%
 
n141676.0%
 
N100654.3%
 
o100654.3%
 
r100654.3%
 
m100654.3%
 
l100654.3%
 
100654.3%
 
(100654.3%
 
3100654.3%
 
-100654.3%
 
6100654.3%
 
)100654.3%
 
T98694.2%
 
c98694.2%
 
h98694.2%
 
y98694.2%
 
p98694.2%
 
e98694.2%
 

Heart_Rate_(rates/min
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2113
Missing (%)9.6%
Memory size172.6 KiB
Normal
10187 
Tachycardia
9783 
ValueCountFrequency (%) 
Normal1018746.1%
 
Tachycardia978344.3%
 
(Missing)21139.6%
 
2021-11-04T17:45:29.588591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:29.789735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:29.933286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length6
Mean length7.927998913
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a4164923.8%
 
r1997011.4%
 
c1956611.2%
 
N101875.8%
 
o101875.8%
 
m101875.8%
 
l101875.8%
 
T97835.6%
 
h97835.6%
 
y97835.6%
 
d97835.6%
 
i97835.6%
 
n42262.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter15510488.6%
 
Uppercase Letter1997011.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1018751.0%
 
T978349.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a4164926.9%
 
r1997012.9%
 
c1956612.6%
 
o101876.6%
 
m101876.6%
 
l101876.6%
 
h97836.3%
 
y97836.3%
 
d97836.3%
 
i97836.3%
 
n42262.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin175074100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a4164923.8%
 
r1997011.4%
 
c1956611.2%
 
N101875.8%
 
o101875.8%
 
m101875.8%
 
l101875.8%
 
T97835.6%
 
h97835.6%
 
y97835.6%
 
d97835.6%
 
i97835.6%
 
n42262.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII175074100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a4164923.8%
 
r1997011.4%
 
c1956611.2%
 
N101875.8%
 
o101875.8%
 
m101875.8%
 
l101875.8%
 
T97835.6%
 
h97835.6%
 
y97835.6%
 
d97835.6%
 
i97835.6%
 
n42262.4%
 

Test_1
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing2127
Missing (%)9.6%
Memory size172.6 KiB
0
19956 
(Missing)
2127 
ValueCountFrequency (%) 
01995690.4%
 
(Missing)21279.6%
 
2021-11-04T17:45:30.080415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Test_2
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing2152
Missing (%)9.7%
Memory size172.6 KiB
0
19931 
(Missing)
2152 
ValueCountFrequency (%) 
01993190.3%
 
(Missing)21529.7%
 
2021-11-04T17:45:30.144835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Test_3
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing2147
Missing (%)9.7%
Memory size172.6 KiB
0
19936 
(Missing)
2147 
ValueCountFrequency (%) 
01993690.3%
 
(Missing)21479.7%
 
2021-11-04T17:45:30.202764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Test_4
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing2140
Missing (%)9.7%
Memory size172.6 KiB
1
19943 
(Missing)
2140 
ValueCountFrequency (%) 
11994390.3%
 
(Missing)21409.7%
 
2021-11-04T17:45:30.256382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Test_5
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing2170
Missing (%)9.8%
Memory size172.6 KiB
0
19913 
(Missing)
2170 
ValueCountFrequency (%) 
01991390.2%
 
(Missing)21709.8%
 
2021-11-04T17:45:30.314505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Parental_consent
Categorical

MISSING

Distinct1
Distinct (%)< 0.1%
Missing2125
Missing (%)9.6%
Memory size172.6 KiB
Yes
19958 
ValueCountFrequency (%) 
Yes1995890.4%
 
(Missing)21259.6%
 
2021-11-04T17:45:30.448856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:30.580509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:30.703637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
Y1995830.1%
 
e1995830.1%
 
s1995830.1%
 
n42506.4%
 
a21253.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4629169.9%
 
Uppercase Letter1995830.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y19958100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1995843.1%
 
s1995843.1%
 
n42509.2%
 
a21254.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin66249100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y1995830.1%
 
e1995830.1%
 
s1995830.1%
 
n42506.4%
 
a21253.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII66249100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
Y1995830.1%
 
e1995830.1%
 
s1995830.1%
 
n42506.4%
 
a21253.2%
 

Follow-up
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2166
Missing (%)9.8%
Memory size172.6 KiB
Low
10040 
High
9877 
ValueCountFrequency (%) 
Low1004045.5%
 
High987744.7%
 
(Missing)21669.8%
 
2021-11-04T17:45:30.997937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:31.294504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:31.438856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.447267129
Min length3

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
L1004013.2%
 
o1004013.2%
 
w1004013.2%
 
H987713.0%
 
i987713.0%
 
g987713.0%
 
h987713.0%
 
n43325.7%
 
a21662.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5620973.8%
 
Uppercase Letter1991726.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L1004050.4%
 
H987749.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1004017.9%
 
w1004017.9%
 
i987717.6%
 
g987717.6%
 
h987717.6%
 
n43327.7%
 
a21663.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin76126100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
L1004013.2%
 
o1004013.2%
 
w1004013.2%
 
H987713.0%
 
i987713.0%
 
g987713.0%
 
h987713.0%
 
n43325.7%
 
a21662.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII76126100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
L1004013.2%
 
o1004013.2%
 
w1004013.2%
 
H987713.0%
 
i987713.0%
 
g987713.0%
 
h987713.0%
 
n43325.7%
 
a21662.8%
 

Gender
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing2173
Missing (%)9.8%
Memory size172.6 KiB
Ambiguous
6695 
Male
6666 
Female
6549 
ValueCountFrequency (%) 
Ambiguous669530.3%
 
Male666630.2%
 
Female654929.7%
 
(Missing)21739.8%
 
2021-11-04T17:45:31.727442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:31.917331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:32.081743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length6.010596386
Min length3

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1976414.9%
 
a1538811.6%
 
u1339010.1%
 
m1324410.0%
 
l1321510.0%
 
A66955.0%
 
b66955.0%
 
i66955.0%
 
g66955.0%
 
o66955.0%
 
s66955.0%
 
M66665.0%
 
F65494.9%
 
n43463.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter11282285.0%
 
Uppercase Letter1991015.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1976417.5%
 
a1538813.6%
 
u1339011.9%
 
m1324411.7%
 
l1321511.7%
 
b66955.9%
 
i66955.9%
 
g66955.9%
 
o66955.9%
 
s66955.9%
 
n43463.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A669533.6%
 
M666633.5%
 
F654932.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin132732100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1976414.9%
 
a1538811.6%
 
u1339010.1%
 
m1324410.0%
 
l1321510.0%
 
A66955.0%
 
b66955.0%
 
i66955.0%
 
g66955.0%
 
o66955.0%
 
s66955.0%
 
M66665.0%
 
F65494.9%
 
n43463.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII132732100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1976414.9%
 
a1538811.6%
 
u1339010.1%
 
m1324410.0%
 
l1321510.0%
 
A66955.0%
 
b66955.0%
 
i66955.0%
 
g66955.0%
 
o66955.0%
 
s66955.0%
 
M66665.0%
 
F65494.9%
 
n43463.3%
 

Birth_asphyxia
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing2139
Missing (%)9.7%
Memory size172.6 KiB
Yes
5106 
No record
5008 
Not available
4986 
No
4844 
ValueCountFrequency (%) 
Yes510623.1%
 
No record500822.7%
 
Not available498622.6%
 
No484421.9%
 
(Missing)21399.7%
 
2021-11-04T17:45:32.355202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:32.542203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:32.724293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length3
Mean length6.399175837
Min length2

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o1984614.0%
 
a1709712.1%
 
e1510010.7%
 
N1483810.5%
 
r100167.1%
 
99947.1%
 
l99727.1%
 
Y51063.6%
 
s51063.6%
 
c50083.5%
 
d50083.5%
 
t49863.5%
 
v49863.5%
 
i49863.5%
 
b49863.5%
 
n42783.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter11137578.8%
 
Uppercase Letter1994414.1%
 
Space Separator99947.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1984617.8%
 
a1709715.4%
 
e1510013.6%
 
r100169.0%
 
l99729.0%
 
s51064.6%
 
c50084.5%
 
d50084.5%
 
t49864.5%
 
v49864.5%
 
i49864.5%
 
b49864.5%
 
n42783.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1483874.4%
 
Y510625.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
9994100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin13131992.9%
 
Common99947.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o1984615.1%
 
a1709713.0%
 
e1510011.5%
 
N1483811.3%
 
r100167.6%
 
l99727.6%
 
Y51063.9%
 
s51063.9%
 
c50083.8%
 
d50083.8%
 
t49863.8%
 
v49863.8%
 
i49863.8%
 
b49863.8%
 
n42783.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
9994100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII141313100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o1984614.0%
 
a1709712.1%
 
e1510010.7%
 
N1483810.5%
 
r100167.1%
 
99947.1%
 
l99727.1%
 
Y51063.6%
 
s51063.6%
 
c50083.5%
 
d50083.5%
 
t49863.5%
 
v49863.5%
 
i49863.5%
 
b49863.5%
 
n42783.0%
 

Autopsy_shows_birth_defect_(if_applicable)
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1026
Missing (%)4.6%
Memory size172.6 KiB
Not applicable
11083 
Yes
3383 
None
3366 
No
3225 
ValueCountFrequency (%) 
Not applicable1108350.2%
 
Yes338315.3%
 
None336615.2%
 
No322514.6%
 
(Missing)10264.6%
 
2021-11-04T17:45:33.157550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:33.478704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:34.115033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length8.527057012
Min length2

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a2319212.3%
 
p2216611.8%
 
l2216611.8%
 
e178329.5%
 
N176749.4%
 
o176749.4%
 
t110835.9%
 
110835.9%
 
i110835.9%
 
c110835.9%
 
b110835.9%
 
n54182.9%
 
Y33831.8%
 
s33831.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter15616382.9%
 
Uppercase Letter2105711.2%
 
Space Separator110835.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1767483.9%
 
Y338316.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a2319214.9%
 
p2216614.2%
 
l2216614.2%
 
e1783211.4%
 
o1767411.3%
 
t110837.1%
 
i110837.1%
 
c110837.1%
 
b110837.1%
 
n54183.5%
 
s33832.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
11083100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin17722094.1%
 
Common110835.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a2319213.1%
 
p2216612.5%
 
l2216612.5%
 
e1783210.1%
 
N1767410.0%
 
o1767410.0%
 
t110836.3%
 
i110836.3%
 
c110836.3%
 
b110836.3%
 
n54183.1%
 
Y33831.9%
 
s33831.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
11083100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII188303100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a2319212.3%
 
p2216611.8%
 
l2216611.8%
 
e178329.5%
 
N176749.4%
 
o176749.4%
 
t110835.9%
 
110835.9%
 
i110835.9%
 
c110835.9%
 
b110835.9%
 
n54182.9%
 
Y33831.8%
 
s33831.8%
 

Place_of_birth
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing2124
Missing (%)9.6%
Memory size172.6 KiB
Institute
10073 
Home
9886 
ValueCountFrequency (%) 
Institute1007345.6%
 
Home988644.8%
 
(Missing)21249.6%
 
2021-11-04T17:45:34.848477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:35.134139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:36.012733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length4
Mean length6.184531087
Min length3

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t3021922.1%
 
e1995914.6%
 
n1432110.5%
 
I100737.4%
 
s100737.4%
 
i100737.4%
 
u100737.4%
 
H98867.2%
 
o98867.2%
 
m98867.2%
 
a21241.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter11661485.4%
 
Uppercase Letter1995914.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I1007350.5%
 
H988649.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t3021925.9%
 
e1995917.1%
 
n1432112.3%
 
s100738.6%
 
i100738.6%
 
u100738.6%
 
o98868.5%
 
m98868.5%
 
a21241.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin136573100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t3021922.1%
 
e1995914.6%
 
n1432110.5%
 
I100737.4%
 
s100737.4%
 
i100737.4%
 
u100737.4%
 
H98867.2%
 
o98867.2%
 
m98867.2%
 
a21241.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII136573100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t3021922.1%
 
e1995914.6%
 
n1432110.5%
 
I100737.4%
 
s100737.4%
 
i100737.4%
 
u100737.4%
 
H98867.2%
 
o98867.2%
 
m98867.2%
 
a21241.6%
 

Folic_acid_details_(peri-conceptional)
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2117
Missing (%)9.6%
Memory size172.6 KiB
Yes
10087 
No
9879 
(Missing)
2117 
ValueCountFrequency (%) 
Yes1008745.7%
 
No987944.7%
 
(Missing)21179.6%
 
2021-11-04T17:45:36.226991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

H/O_serious_maternal_illness
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2152
Missing (%)9.7%
Memory size172.6 KiB
No
10012 
Yes
9919 
(Missing)
2152 
ValueCountFrequency (%) 
No1001245.3%
 
Yes991944.9%
 
(Missing)21529.7%
 
2021-11-04T17:45:36.321653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

H/O_radiation_exposure_(x-ray)
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing2153
Missing (%)9.7%
Memory size172.6 KiB
Not applicable
5029 
No
5005 
Yes
4980 
-
4916 
ValueCountFrequency (%) 
Not applicable502922.8%
 
No500522.7%
 
Yes498022.6%
 
-491622.3%
 
(Missing)21539.7%
 
2021-11-04T17:45:36.570619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:36.828618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:37.251823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length3
Mean length4.83317484
Min length1

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1221111.4%
 
p100589.4%
 
l100589.4%
 
N100349.4%
 
o100349.4%
 
e100099.4%
 
t50294.7%
 
50294.7%
 
i50294.7%
 
c50294.7%
 
b50294.7%
 
Y49804.7%
 
s49804.7%
 
-49164.6%
 
n43064.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8177276.6%
 
Uppercase Letter1501414.1%
 
Space Separator50294.7%
 
Dash Punctuation49164.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1003466.8%
 
Y498033.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1221114.9%
 
p1005812.3%
 
l1005812.3%
 
o1003412.3%
 
e1000912.2%
 
t50296.2%
 
i50296.2%
 
c50296.2%
 
b50296.2%
 
s49806.1%
 
n43065.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
5029100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4916100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin9678690.7%
 
Common99459.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1221112.6%
 
p1005810.4%
 
l1005810.4%
 
N1003410.4%
 
o1003410.4%
 
e1000910.3%
 
t50295.2%
 
i50295.2%
 
c50295.2%
 
b50295.2%
 
Y49805.1%
 
s49805.1%
 
n43064.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
502950.6%
 
-491649.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII106731100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1221111.4%
 
p100589.4%
 
l100589.4%
 
N100349.4%
 
o100349.4%
 
e100099.4%
 
t50294.7%
 
50294.7%
 
i50294.7%
 
c50294.7%
 
b50294.7%
 
Y49804.7%
 
s49804.7%
 
-49164.6%
 
n43064.0%
 

H/O_substance_abuse
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing2195
Missing (%)9.9%
Memory size172.6 KiB
-
5042 
No
5033 
Yes
4975 
Not applicable
4838 
ValueCountFrequency (%) 
-504222.8%
 
No503322.8%
 
Yes497522.5%
 
Not applicable483821.9%
 
(Missing)21959.9%
 
2021-11-04T17:45:37.566652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:37.916903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:38.593762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length3
Mean length4.725354345
Min length1

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1187111.4%
 
N98719.5%
 
o98719.5%
 
e98139.4%
 
p96769.3%
 
l96769.3%
 
-50424.8%
 
Y49754.8%
 
s49754.8%
 
t48384.6%
 
48384.6%
 
i48384.6%
 
c48384.6%
 
b48384.6%
 
n43904.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7962476.3%
 
Uppercase Letter1484614.2%
 
Dash Punctuation50424.8%
 
Space Separator48384.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N987166.5%
 
Y497533.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1187114.9%
 
o987112.4%
 
e981312.3%
 
p967612.2%
 
l967612.2%
 
s49756.2%
 
t48386.1%
 
i48386.1%
 
c48386.1%
 
b48386.1%
 
n43905.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4838100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-5042100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin9447090.5%
 
Common98809.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1187112.6%
 
N987110.4%
 
o987110.4%
 
e981310.4%
 
p967610.2%
 
l967610.2%
 
Y49755.3%
 
s49755.3%
 
t48385.1%
 
i48385.1%
 
c48385.1%
 
b48385.1%
 
n43904.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
-504251.0%
 
483849.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII104350100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1187111.4%
 
N98719.5%
 
o98719.5%
 
e98139.4%
 
p96769.3%
 
l96769.3%
 
-50424.8%
 
Y49754.8%
 
s49754.8%
 
t48384.6%
 
48384.6%
 
i48384.6%
 
c48384.6%
 
b48384.6%
 
n43904.2%
 

Assisted_conception_IVF/ART
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2122
Missing (%)9.6%
Memory size172.6 KiB
Yes
10012 
No
9949 
(Missing)
2122 
ValueCountFrequency (%) 
Yes1001245.3%
 
No994945.1%
 
(Missing)21229.6%
 
2021-11-04T17:45:38.826903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

History_of_anomalies_in_previous_pregnancies
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2172
Missing (%)9.8%
Memory size172.6 KiB
Yes
10082 
No
9829 
(Missing)
2172 
ValueCountFrequency (%) 
Yes1008245.7%
 
No982944.5%
 
(Missing)21729.8%
 
2021-11-04T17:45:38.919882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

No._of_previous_abortion
Real number (ℝ≥0)

MISSING
ZEROS

Distinct5
Distinct (%)< 0.1%
Missing2162
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean2.003062095
Minimum0
Maximum4
Zeros3964
Zeros (%)18.0%
Memory size172.6 KiB
2021-11-04T17:45:39.220573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.411918808
Coefficient of variation (CV)0.7048801987
Kurtosis-1.290355235
Mean2.003062095
Median Absolute Deviation (MAD)1
Skewness-0.001030905292
Sum39903
Variance1.993514719
MonotocityNot monotonic
2021-11-04T17:45:39.493551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
2411718.6%
 
4400518.1%
 
0396418.0%
 
1392817.8%
 
3390717.7%
 
(Missing)21629.8%
 
ValueCountFrequency (%) 
0396418.0%
 
1392817.8%
 
2411718.6%
 
3390717.7%
 
4400518.1%
 
ValueCountFrequency (%) 
4400518.1%
 
3390717.7%
 
2411718.6%
 
1392817.8%
 
0396418.0%
 

Birth_defects
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2154
Missing (%)9.8%
Memory size172.6 KiB
Singular
9977 
Multiple
9952 
ValueCountFrequency (%) 
Singular997745.2%
 
Multiple995245.1%
 
(Missing)21549.8%
 
2021-11-04T17:45:39.769089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:39.906079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:40.171608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.512294525
Min length3

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l2988118.0%
 
u1992912.0%
 
i1992912.0%
 
n142858.6%
 
a121317.3%
 
S99776.0%
 
g99776.0%
 
r99776.0%
 
M99526.0%
 
t99526.0%
 
p99526.0%
 
e99526.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14596588.0%
 
Uppercase Letter1992912.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l2988120.5%
 
u1992913.7%
 
i1992913.7%
 
n142859.8%
 
a121318.3%
 
g99776.8%
 
r99776.8%
 
t99526.8%
 
p99526.8%
 
e99526.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S997750.1%
 
M995249.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin165894100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l2988118.0%
 
u1992912.0%
 
i1992912.0%
 
n142858.6%
 
a121317.3%
 
S99776.0%
 
g99776.0%
 
r99776.0%
 
M99526.0%
 
t99526.0%
 
p99526.0%
 
e99526.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII165894100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l2988118.0%
 
u1992912.0%
 
i1992912.0%
 
n142858.6%
 
a121317.3%
 
S99776.0%
 
g99776.0%
 
r99776.0%
 
M99526.0%
 
t99526.0%
 
p99526.0%
 
e99526.0%
 

White_Blood_cell_count_(thousand_per_microliter)
Real number (ℝ≥0)

MISSING

Distinct17277
Distinct (%)86.7%
Missing2148
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean7.486223987
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Memory size172.6 KiB
2021-11-04T17:45:40.489099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15.424703056
median7.477132167
Q39.526151897
95-th percentile12
Maximum12
Range9
Interquartile range (IQR)4.10144884

Descriptive statistics

Standard deviation2.653392652
Coefficient of variation (CV)0.3544367169
Kurtosis-0.974504467
Mean7.486223987
Median Absolute Deviation (MAD)2.051562736
Skewness0.006638939182
Sum149237.8752
Variance7.040492567
MonotocityNot monotonic
2021-11-04T17:45:40.823968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
313336.0%
 
1213276.0%
 
5.0119050941< 0.1%
 
5.3574751391< 0.1%
 
3.9725194391< 0.1%
 
4.6294335871< 0.1%
 
6.7248892561< 0.1%
 
5.4366223681< 0.1%
 
7.9115259711< 0.1%
 
9.7019247761< 0.1%
 
7.1224331< 0.1%
 
7.2747366671< 0.1%
 
10.824887821< 0.1%
 
7.2895130591< 0.1%
 
9.5563622341< 0.1%
 
7.1495179621< 0.1%
 
9.5062822721< 0.1%
 
9.5914987481< 0.1%
 
5.0032733211< 0.1%
 
7.5264516811< 0.1%
 
7.5743357941< 0.1%
 
7.4967531821< 0.1%
 
7.4629329281< 0.1%
 
4.5614221071< 0.1%
 
7.6808029071< 0.1%
 
Other values (17252)1725278.1%
 
(Missing)21489.7%
 
ValueCountFrequency (%) 
313336.0%
 
3.0007361311< 0.1%
 
3.0014569881< 0.1%
 
3.0036658541< 0.1%
 
3.0038565481< 0.1%
 
3.005595471< 0.1%
 
3.0056215391< 0.1%
 
3.0059675251< 0.1%
 
3.0063149051< 0.1%
 
3.0083127621< 0.1%
 
ValueCountFrequency (%) 
1213276.0%
 
11.999857471< 0.1%
 
11.999652981< 0.1%
 
11.999292931< 0.1%
 
11.996706831< 0.1%
 
11.996677631< 0.1%
 
11.996100311< 0.1%
 
11.995467661< 0.1%
 
11.995346471< 0.1%
 
11.995323181< 0.1%
 

Blood_test_result
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing2145
Missing (%)9.7%
Memory size172.6 KiB
slightly abnormal
5128 
normal
4954 
inconclusive
4952 
abnormal
4904 
ValueCountFrequency (%) 
slightly abnormal512823.2%
 
normal495422.4%
 
inconclusive495222.4%
 
abnormal490422.2%
 
(Missing)21459.7%
 
2021-11-04T17:45:41.104250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:41.271616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:41.520255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length8
Mean length10.05257438
Min length3

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l3019413.6%
 
n2918013.1%
 
a2716312.2%
 
o199389.0%
 
i150326.8%
 
r149866.8%
 
m149866.8%
 
s100804.5%
 
b100324.5%
 
c99044.5%
 
g51282.3%
 
h51282.3%
 
t51282.3%
 
y51282.3%
 
51282.3%
 
u49522.2%
 
v49522.2%
 
e49522.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter21686397.7%
 
Space Separator51282.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l3019413.9%
 
n2918013.5%
 
a2716312.5%
 
o199389.2%
 
i150326.9%
 
r149866.9%
 
m149866.9%
 
s100804.6%
 
b100324.6%
 
c99044.6%
 
g51282.4%
 
h51282.4%
 
t51282.4%
 
y51282.4%
 
u49522.3%
 
v49522.3%
 
e49522.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
5128100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin21686397.7%
 
Common51282.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l3019413.9%
 
n2918013.5%
 
a2716312.5%
 
o199389.2%
 
i150326.9%
 
r149866.9%
 
m149866.9%
 
s100804.6%
 
b100324.6%
 
c99044.6%
 
g51282.4%
 
h51282.4%
 
t51282.4%
 
y51282.4%
 
u49522.3%
 
v49522.3%
 
e49522.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
5128100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII221991100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l3019413.6%
 
n2918013.1%
 
a2716312.2%
 
o199389.0%
 
i150326.8%
 
r149866.8%
 
m149866.8%
 
s100804.5%
 
b100324.5%
 
c99044.5%
 
g51282.3%
 
h51282.3%
 
t51282.3%
 
y51282.3%
 
51282.3%
 
u49522.2%
 
v49522.2%
 
e49522.2%
 

Symptom_1
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2155
Missing (%)9.8%
Memory size172.6 KiB
1
11807 
0
8121 
(Missing)
2155 
ValueCountFrequency (%) 
11180753.5%
 
0812136.8%
 
(Missing)21559.8%
 
2021-11-04T17:45:41.695409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Symptom_2
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2222
Missing (%)10.1%
Memory size172.6 KiB
1
10961 
0
8900 
(Missing)
2222 
ValueCountFrequency (%) 
11096149.6%
 
0890040.3%
 
(Missing)222210.1%
 
2021-11-04T17:45:41.777925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Symptom_3
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2101
Missing (%)9.5%
Memory size172.6 KiB
1
10715 
0
9267 
(Missing)
2101 
ValueCountFrequency (%) 
11071548.5%
 
0926742.0%
 
(Missing)21019.5%
 
2021-11-04T17:45:41.854012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Symptom_4
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2113
Missing (%)9.6%
Memory size172.6 KiB
0
10030 
1
9940 
(Missing)
2113 
ValueCountFrequency (%) 
01003045.4%
 
1994045.0%
 
(Missing)21139.6%
 
2021-11-04T17:45:41.931692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Symptom_5
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing2153
Missing (%)9.7%
Memory size172.6 KiB
0
10724 
1
9206 
(Missing)
2153 
ValueCountFrequency (%) 
01072448.6%
 
1920641.7%
 
(Missing)21539.7%
 
2021-11-04T17:45:42.016724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Genetic_Disorder
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing2146
Missing (%)9.7%
Memory size172.6 KiB
Mitochondrial genetic inheritance disorders
10202 
Single-gene inheritance diseases
7664 
Multifactorial genetic inheritance disorders
2071 
ValueCountFrequency (%) 
Mitochondrial genetic inheritance disorders1020246.2%
 
Single-gene inheritance diseases766434.7%
 
Multifactorial genetic inheritance disorders20719.4%
 
(Missing)21469.7%
 
2021-11-04T17:45:42.194183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:42.363108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:42.518047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length44
Median length43
Mean length35.38903229
Min length3

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e11501314.7%
 
i10429413.3%
 
n8196910.5%
 
r567567.3%
 
521476.7%
 
s475386.1%
 
t465546.0%
 
c444835.7%
 
a440915.6%
 
d424125.4%
 
o347484.4%
 
h301393.9%
 
g276013.5%
 
l220082.8%
 
M122731.6%
 
S76641.0%
 
-76641.0%
 
u20710.3%
 
f20710.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter70174889.8%
 
Space Separator521476.7%
 
Uppercase Letter199372.6%
 
Dash Punctuation76641.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M1227361.6%
 
S766438.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e11501316.4%
 
i10429414.9%
 
n8196911.7%
 
r567568.1%
 
s475386.8%
 
t465546.6%
 
c444836.3%
 
a440916.3%
 
d424126.0%
 
o347485.0%
 
h301394.3%
 
g276013.9%
 
l220083.1%
 
u20710.3%
 
f20710.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
52147100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-7664100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin72168592.3%
 
Common598117.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e11501315.9%
 
i10429414.5%
 
n8196911.4%
 
r567567.9%
 
s475386.6%
 
t465546.5%
 
c444836.2%
 
a440916.1%
 
d424125.9%
 
o347484.8%
 
h301394.2%
 
g276013.8%
 
l220083.0%
 
M122731.7%
 
S76641.1%
 
u20710.3%
 
f20710.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
5214787.2%
 
-766412.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII781496100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e11501314.7%
 
i10429413.3%
 
n8196910.5%
 
r567567.3%
 
521476.7%
 
s475386.1%
 
t465546.0%
 
c444835.7%
 
a440915.6%
 
d424125.4%
 
o347484.4%
 
h301393.9%
 
g276013.5%
 
l220082.8%
 
M122731.6%
 
S76641.0%
 
-76641.0%
 
u20710.3%
 
f20710.3%
 

Disorder_Subclass
Categorical

HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing2168
Missing (%)9.8%
Memory size172.6 KiB
Leigh syndrome
5160 
Mitochondrial myopathy
4405 
Cystic fibrosis
3448 
Tay-Sachs
2833 
Diabetes
1817 
Other values (4)
2252 
ValueCountFrequency (%) 
Leigh syndrome516023.4%
 
Mitochondrial myopathy440519.9%
 
Cystic fibrosis344815.6%
 
Tay-Sachs283312.8%
 
Diabetes18178.2%
 
Hemochromatosis13556.1%
 
Leber's hereditary optic neuropathy6482.9%
 
Alzheimer's1520.7%
 
Cancer970.4%
 
(Missing)21689.8%
 
2021-11-04T17:45:42.730100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-04T17:45:42.889096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:43.123485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length14
Mean length14.15867409
Min length3

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i289349.3%
 
o271848.7%
 
s236647.6%
 
y215476.9%
 
a212096.8%
 
h196066.3%
 
e189506.1%
 
t173745.6%
 
r172095.5%
 
149574.8%
 
n146464.7%
 
c127864.1%
 
m124274.0%
 
d102133.3%
 
b59131.9%
 
L58081.9%
 
p57011.8%
 
g51601.7%
 
l45571.5%
 
M44051.4%
 
C35451.1%
 
f34481.1%
 
T28330.9%
 
-28330.9%
 
S28330.9%
 
Other values (6)49241.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter27132886.8%
 
Uppercase Letter227487.3%
 
Space Separator149574.8%
 
Dash Punctuation28330.9%
 
Other Punctuation8000.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L580825.5%
 
M440519.4%
 
C354515.6%
 
T283312.5%
 
S283312.5%
 
D18178.0%
 
H13556.0%
 
A1520.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i2893410.7%
 
o2718410.0%
 
s236648.7%
 
y215477.9%
 
a212097.8%
 
h196067.2%
 
e189507.0%
 
t173746.4%
 
r172096.3%
 
n146465.4%
 
c127864.7%
 
m124274.6%
 
d102133.8%
 
b59132.2%
 
p57012.1%
 
g51601.9%
 
l45571.7%
 
f34481.3%
 
u6480.2%
 
z1520.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'800100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
14957100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2833100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin29407694.1%
 
Common185905.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i289349.8%
 
o271849.2%
 
s236648.0%
 
y215477.3%
 
a212097.2%
 
h196066.7%
 
e189506.4%
 
t173745.9%
 
r172095.9%
 
n146465.0%
 
c127864.3%
 
m124274.2%
 
d102133.5%
 
b59132.0%
 
L58082.0%
 
p57011.9%
 
g51601.8%
 
l45571.5%
 
M44051.5%
 
C35451.2%
 
f34481.2%
 
T28331.0%
 
S28331.0%
 
D18170.6%
 
H13550.5%
 
Other values (3)9520.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
1495780.5%
 
-283315.2%
 
'8004.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII312666100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i289349.3%
 
o271848.7%
 
s236647.6%
 
y215476.9%
 
a212096.8%
 
h196066.3%
 
e189506.1%
 
t173745.6%
 
r172095.5%
 
149574.8%
 
n146464.7%
 
c127864.1%
 
m124274.0%
 
d102133.3%
 
b59131.9%
 
L58081.9%
 
p57011.8%
 
g51601.7%
 
l45571.5%
 
M44051.4%
 
C35451.1%
 
f34481.1%
 
T28330.9%
 
-28330.9%
 
S28330.9%
 
Other values (6)49241.6%
 

Interactions

2021-11-04T17:44:56.412712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:56.720718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:57.062080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:57.442621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:57.812957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:58.179851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:58.556276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:59.012308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:59.363299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:44:59.767557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:00.139161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:00.556568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:00.924814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:01.259139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:01.595607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:01.942650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:02.257350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:02.517198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:02.764316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:03.008350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:03.709198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:03.999703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:04.277423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:04.619429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:04.953932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:05.215542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:05.780959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:06.023530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:06.535147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:06.930595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:07.163891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:07.375202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:07.608539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:07.836923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:08.131969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:08.367413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-11-04T17:45:43.401252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-04T17:45:43.890502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-04T17:45:44.325655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-04T17:45:44.841901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-04T17:45:45.529452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-04T17:45:09.274401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:13.171696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:16.168378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-04T17:45:19.862565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

Patient_IdPatient_AgeGenes_in_mothers_sideInherited_from_fatherMaternal_genePaternal_geneBlood_cell_count_(mcL)Patient_First_NameFamily_NameFathers_nameMothers_ageFathers_ageInstitute_NameLocation_of_InstituteStatusRespiratory_Rate_(breaths/min)Heart_Rate_(rates/minTest_1Test_2Test_3Test_4Test_5Parental_consentFollow-upGenderBirth_asphyxiaAutopsy_shows_birth_defect_(if_applicable)Place_of_birthFolic_acid_details_(peri-conceptional)H/O_serious_maternal_illnessH/O_radiation_exposure_(x-ray)H/O_substance_abuseAssisted_conception_IVF/ARTHistory_of_anomalies_in_previous_pregnanciesNo._of_previous_abortionBirth_defectsWhite_Blood_cell_count_(thousand_per_microliter)Blood_test_resultSymptom_1Symptom_2Symptom_3Symptom_4Symptom_5Genetic_DisorderDisorder_Subclass
0PID0x64182.0YesNoYesNo4.760603RichardNaNLarreNaNNaNBoston Specialty & Rehabilitation Hospital55 FRUIT ST\nCENTRAL, MA 02114\n(42.36247485742686, -71.06924724545246)AliveNormal (30-60)Normal0.0NaNNaN1.00.0YesHighNaNNaNNot applicableInstituteNoNaNNoNoNoYesNaNNaN9.857562NaN1.01.01.01.01.0Mitochondrial genetic inheritance disordersLeber's hereditary optic neuropathy
1PID0x25d54.0YesYesNoNo4.910669MikeNaNBrycenNaN23.0St. Margaret's Hospital For Women1515 COMMONWEALTH AV\nALLSTON/BRIGHTON, MA 02135\n(42.34665771451756, -71.14136122385321)DeceasedTachypneaNormalNaN0.00.01.00.0YesHighNaNNoNoneNaNYesYesNot applicableNot applicableNoYesNaNMultiple5.522560normal1.0NaN1.01.00.0NaNCystic fibrosis
2PID0x4a826.0YesNoNoNo4.893297KimberlyNaNNashon41.022.0NaN-AliveNormal (30-60)Tachycardia0.00.00.01.00.0YesLowNaNNo recordNot applicableNaNYesNoYesNaNYesYes4.0SingularNaNnormal0.01.01.01.01.0Multifactorial genetic inheritance disordersDiabetes
3PID0x4ac812.0YesNoYesNo4.705280JefferyHoelscherAayaan21.0NaNNaN55 FRUIT ST\nCENTRAL, MA 02114\n(42.36247485742686, -71.06924724545246)DeceasedTachypneaNormal0.00.00.01.00.0YesHighMaleNot availableNoInstituteNoYes-Not applicableNaNYes1.0Singular7.919321inconclusive0.00.01.00.00.0Mitochondrial genetic inheritance disordersLeigh syndrome
4PID0x1bf711.0YesNoNaNYes4.720703JohannaStutzmanSuave32.0NaNCarney Hospital300 LONGWOOD AV\nFENWAY/KENMORE, MA 02115\n(42.337592548462226, -71.10472284437952)AliveTachypneaTachycardia0.00.00.01.00.0NaNLowMaleNot availableNot applicableInstituteNoYes-Not applicableYesNo4.0Multiple4.098210NaN0.00.00.00.0NaNMultifactorial genetic inheritance disordersCancer
5PID0x44fe14.0YesNoYesNo5.103188RichardNaNColestonNaNNaNMassachusetts General Hospital55 FRUIT ST\nCENTRAL, MA 02114\n(42.36247485742686, -71.06924724545246)DeceasedNaNNormal0.00.00.01.00.0YesLowFemaleNot availableNoneInstituteNoNoNoNoNaNNo0.0Multiple10.272230normal1.00.00.01.00.0Single-gene inheritance diseasesCystic fibrosis
6PID0x28de3.0YesNoYesYes4.901080MaryNaNAydunNaN63.0Not applicable-AliveNormal (30-60)NaNNaN0.00.01.00.0NaNLowMaleNo recordNot applicableHomeNaNYesNoNot applicableYesNo3.0Multiple6.825974normal0.00.00.00.00.0Single-gene inheritance diseasesTay-Sachs
7PID0x4f8f3.0NoNoYesYes4.964816EmmaBryantKeng40.0NaNNot applicable-AliveTachypneaNormal0.00.0NaN1.00.0YesLowNaNNo recordNot applicableHomeYesYesNo-NoYes1.0Singular9.836352inconclusive0.00.01.0NaN0.0Single-gene inheritance diseasesTay-Sachs
8PID0x8ce311.0NoNoYesNo5.209058WillieCamachoTr45.044.0Lemuel Shattuck Hospital125 NASHUA ST\nCENTRAL, MA 02114\n(42.36764789068138, -71.06564730220646)AliveTachypneaTachycardia0.00.00.01.00.0YesLowMaleYesNot applicableInstituteYesYesNoNoNoYes0.0Multiple6.669552slightly abnormal1.01.01.00.01.0Mitochondrial genetic inheritance disordersLeigh syndrome
9PID0x86604.0NoYesYesYes4.752272JohnSandovalGregori44.042.0Shriners Burns Institute1200 Centre St\nRoslindale, MA 02131\n(42.29738386053219, -71.13150465441208)AliveTachypneaTachycardia0.00.00.01.00.0YesLowMaleNoNot applicableInstituteYesNoNoNoYesYes1.0Multiple6.397702abnormal0.00.01.01.01.0Multifactorial genetic inheritance disordersDiabetes

Last rows

Patient_IdPatient_AgeGenes_in_mothers_sideInherited_from_fatherMaternal_genePaternal_geneBlood_cell_count_(mcL)Patient_First_NameFamily_NameFathers_nameMothers_ageFathers_ageInstitute_NameLocation_of_InstituteStatusRespiratory_Rate_(breaths/min)Heart_Rate_(rates/minTest_1Test_2Test_3Test_4Test_5Parental_consentFollow-upGenderBirth_asphyxiaAutopsy_shows_birth_defect_(if_applicable)Place_of_birthFolic_acid_details_(peri-conceptional)H/O_serious_maternal_illnessH/O_radiation_exposure_(x-ray)H/O_substance_abuseAssisted_conception_IVF/ARTHistory_of_anomalies_in_previous_pregnanciesNo._of_previous_abortionBirth_defectsWhite_Blood_cell_count_(thousand_per_microliter)Blood_test_resultSymptom_1Symptom_2Symptom_3Symptom_4Symptom_5Genetic_DisorderDisorder_Subclass
22073PID0xbd13.0YesYesNoYes4.874635RosaNaNDonovin44.062.0Not applicable-AliveTachypneaTachycardia0.0NaN0.01.00.0YesNaNNaNNo recordNot applicableHomeNoNaNNoNoYesNo1.0NaNNaNnormal0.00.00.01.01.0NaNLeigh syndrome
22074PID0x6a0a4.0NoNoNaNNo4.789307RandyHowellJavontay35.051.0Beth Israel Deaconess Medical Center West Cam88 EAST NEWTON ST\nSOUTH END, MA 02118\n(42.3371094801158, -71.07139912234962)AliveTachypneaNormal0.00.00.01.00.0YesLowMaleYesNot applicableInstituteNoNo-NoNoNo3.0MultipleNaNnormal0.00.01.00.00.0Single-gene inheritance diseasesHemochromatosis
22075PID0x5f5610.0NoNoYesYes4.643860EdwardThomasEoghan49.0NaNNot applicable-DeceasedNaNNormal0.00.00.01.00.0NaNLowNaNNoYesHomeNoNaNNaNNot applicableYesYes2.0Multiple9.581455abnormal1.00.00.00.0NaNMitochondrial genetic inheritance disordersMitochondrial myopathy
22076PID0x26b40.0YesNoYesNo4.931758SamuelNaNKirilNaN50.0Lemuel Shattuck Hospital88 EAST NEWTON ST\nSOUTH END, MA 02118\n(42.3371094801158, -71.07139912234962)AliveNormal (30-60)Tachycardia0.00.00.01.00.0YesLowFemaleNo recordNot applicableInstituteNoNoNot applicableNoYesYes1.0Singular11.649052abnormal1.01.00.01.00.0Mitochondrial genetic inheritance disordersLeigh syndrome
22077PID0x36569.0NoYesYesYes5.012599EdwardHurstQuientin47.0NaNNot applicable-DeceasedNaNNormal0.0NaN0.01.00.0YesNaNAmbiguousNo recordYesHomeYesNoNoNot applicableYesYesNaNNaN12.000000slightly abnormalNaN1.00.00.00.0Mitochondrial genetic inheritance disordersLeigh syndrome
22078PID0x55984.0YesYesYesNo5.258298LynnNaNAlhassane35.064.0Franciscan Children's Hospital1153 CENTRE ST\nJAMAICA PLAIN, MA 02130\n(42.30021828265608, -71.12789683059322)DeceasedNormal (30-60)TachycardiaNaN0.0NaN1.00.0YesHighFemaleNoNoInstituteNaNNoNot applicableNoYesNo3.0Multiple6.584811inconclusive0.00.01.00.00.0Mitochondrial genetic inheritance disordersLeigh syndrome
22079PID0x19cb8.0NoYesNoYes4.974220MatthewFarleyDartanionNaN56.0Faulkner Hospital170 MORTON ST\nROSLINDALE, MA 02130\n(42.30025000839615, -71.10737910445549)AliveNormal (30-60)NormalNaN0.0NaN1.0NaNNaNHighAmbiguousNoNot applicableInstituteYesYesNo-YesNo2.0Multiple7.041556inconclusive1.01.01.01.00.0Multifactorial genetic inheritance disordersDiabetes
22080PID0x3c4f8.0YesNoYesNo5.186470JohnNaNCavani35.051.0Not applicable-DeceasedTachypneaNormal0.00.00.01.0NaNYesHighMaleNoNoneHomeNoNoNaNNoNoNo2.0Singular7.715464normal0.00.00.01.0NaNMitochondrial genetic inheritance disordersMitochondrial myopathy
22081PID0x13a7.0YesNoYesYes4.858543SharonNaNBomer19.0NaNNot applicable-AliveTachypneaTachycardia0.00.00.01.00.0YesHighMaleNo recordNot applicableHomeYesYes-YesYesNo1.0Multiple8.437670abnormal1.01.01.00.00.0NaNLeigh syndrome
22082PID0x933211.0YesNoNoNo4.738067AndrewMoseEban32.062.0Hebrew Rehabilitation Center300 LONGWOOD AV\nFENWAY/KENMORE, MA 02115\n(42.337592548462226, -71.10472284437952)DeceasedNormal (30-60)Normal0.00.00.01.00.0YesHighFemaleYesNoneInstituteYesYesNot applicableNoYesYes4.0Singular11.188371normal1.00.01.01.01.0Multifactorial genetic inheritance disordersDiabetes